In digital imaging systems, color management is the controlled conversion between the color representations of various devices, such as image scanners, digital cameras, monitors, TV screens, film printers, office printers, offset presses, corresponding media, and the like. Although the primary goal of color management is to produce accurate color, due to the redundancy resulting from the use of four or more colorants, additional functionality may be obtained. One example of additional functionality that a color management system can provide is improved smoothness of an image. Empirical or interpolation-based approaches treat the print device as a black box between inputs and outputs. Analytical first principle approaches attempt to characterize the device color response using the fewest number of measurements to arrive at analytical functions that physically represent the process. Both approaches are capable of predicting the response of the device for a variety of input images. However, as the number of color separations increases, analytical first principle models require more time and effort to develop. Instead, empirical data-rich methods are utilized, namely, parameterized multi-variable and cluster models. There are advantages in the cluster model over other empirical approaches. Such advantages include, for example, the ability to better represent the response of a marking device with existing models, and/or the ability to utilize simpler models. It can take significant effort to build a full cluster model.
Accordingly, what is needed in this art are systems and methods for updating a cluster model in a color management system with less effort than would be required to rebuild the cluster model.